Shadowing Practice: Database vs Data Warehouse vs Data Lake | Explaining the Differences - Learn English Speaking with Video
Ders oluşturuluyor...
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We live in a world that runs on data.
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It's how Amazon and Netflix know which movies and products to recommend, how Starbucks manages a global supply chain,
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and how Uber connects drivers with passengers in real time.
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But the thing is, data skills aren't just for tech companies or professional analysts anymore.
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Everyone works with data to some degree, and everyone can benefit from data literacy skills.
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In this video, we're covering an important topic that will help you take your data literacy to the next level.
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Alright, so accessing data is one thing, but organizing and storing it is a completely different beast.
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So at a high level, some of the most common storage modes that you're likely to encounter include flat files, databases, data warehouses, and data lakes.
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Now, there are other, less common storage methods and hybrid variations of these as well, but our goal here is really just to paint some broad strokes
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and help you understand the key similarities and differences between them.
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Now, the simplest method of data storage is a flat file,
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which is typically a static tabular data extract saved either to a local drive or cloud storage like Google Drive or OneDrive.
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And this is very common for one off projects or quick ad hoc analyses that don't require multiple data sources, ongoing maintenance or complex data models.
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The most common types of flat files you'll see are CSVs and Excel workbooks.
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Now, a database is a collection of related tables stored in a database management system, or DBMS.
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Databases are typically used for recording and collecting data tied to a single application or business process,
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like capturing transactional records or real-time website activity.
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And they're usually optimized for online transactional processing,
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known as OLTP, meaning that they're built to receive and store data as efficiently as possible, but aren't really ideal for querying or analysis.
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Common database options include MySQL, Amazon RDS, Microsoft Access or SQL Server, and many, many more.
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A data warehouse, on the other hand, is a database
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or a collection of data sourced from multiple databases that's typically structured to support specific analytics needs
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and is optimized for online analytical processing, or OLAP.
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That basically just means it's built in a way
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that makes it very easy and very fast for users to access the data they need for analytical purposes.
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examples of data warehouse tools include Amazon Redshift, Google BigQuery, Snowflake, Teradata, and others.
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And finally we have data lakes which are essentially a repository of both structured
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and unstructured data sources often stored in their raw unprocessed state.
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So we're talking about everything from CSV files to MP4s, PDFs, text documents, and so on.
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Instead of getting Being cleaned and prepped on the way in, data is typically extracted from the lake and transformed for specific purposes,
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often relating to machine learning or AI.
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Some examples of data lakes include Amazon S3, Azure Data Lake, Google Cloud, and more.
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Now, if you're feeling a little overwhelmed about what this all means, no need to worry.
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I've worked in data analytics and business intelligence for over 15 years, and I still get confused by this stuff.
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The good news is that unless you plan to become a data engineer, it's extremely unlikely that you would be the one building or configuring databases or warehouses.
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In fact, in most cases, data engineers or architects are the ones primarily responsible for standing up these systems
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and building the pipelines and automations that help the data flow from one place to another.
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If you enjoyed this content and want to see more, we've got a brand new Data Literacy Foundations course, and it's entirely free.
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You can check it out at mavenanalytics.io.
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So whether you're an individual looking to build confidence, a leader seeking to empower and upskill your team, or a data professional just trying to to stay ahead of the curve,
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this is the course for you.
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We've got a lot to cover, so let's dive in.
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Bu dersin kelimeleri ve konuşma notları
Bu videoda gölgeleme çalışması için 42 cümle ve 674 kelime var. Konuşma bölümü 4:14 sürüyor. Konuşmacı dakikada yaklaşık 159 kelimeyle, günlük konuşmaya yakın doğal bir hızda konuşuyor. Kelimelerin yalnızca %78’i İngilizcede en sık kullanılan 3.000 kelime arasında, bu yüzden kelime dağarcığı zorlayıcı.
Bu videodaki önemli kelimeler
Videodan daha az kullanılan 15 kelime, telaffuzu ve anlamıyla:
- database /ˈdeɪtəˌbeɪs/ (isim) — veritabanı, veri tabanı. A collection of (usually) organized information in a regular structure, usually but not necessarily in a machine-readable format accessible by a computer.
- warehouse /ˈwɛə(ɹ)haʊs/ (isim) — antrepo, depo. A facility for storing large amounts of merchandise or products.
- literacy /ˈlɪtəɹəsi/ (isim) — okuryazarlık, okuma yazma. The ability to read and write.
- cloud /ˈklaʊ̯d/ (isim) — bulut. A visible mass of water droplets suspended in the air.
- skill /skɪl/ (isim) — beceri, maharet. A capacity to do something well; a technique, an ability, usually acquired or learned, as opposed to abilities that are regarded as innate.
- extract /ˈɛkstɹækt/ (isim) — ekstre. Something that is extracted or drawn out.
- topic /ˈtɑpɪk/ (isim) — mevzu, tema. A subject; a theme; a category or general area of interest.
- confused /kənˈfjuːzd/ (sıfat) — şaşkın, şaşırmış. unable to think clearly or understand
- capture /ˈkæp.(t)ʃɚ/ (fiil) — ele geçirmek. To take control of; to seize by force or stratagem.
- passenger /ˈpæs.ɪn.d͡ʒɚ/ (isim) — yolcu. One who rides or travels in a vehicle, but who does not operate it and is not a member of the crew.
- collect /ˈkɑlɪkt/ (fiil) — katılmak, toplamak. To gather together; amass.
- beast /biːst/ (isim) — canavar, hayvan. Any four-footed land mammal.
- encounter /ɪnˈkaʊntɚ/ (fiil) — rastlamak. To meet (someone) or find (something), especially unexpectedly.
- curve /kɚv/ (isim) — kıvrım. A bend, such as in a road.
- dive /daɪv/ (fiil) — dalmak. To swim under water.
Duyacağınız deyimsel fiiller
- stand up (fiil) — satmak. To rise from a lying or sitting position.
Dikkat edilecek telaffuzlar
Konuşmacı you're, aren't, they're gibi kısaltılmış biçimleri 14 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “sh” ve “zh” sesleri: capture /ˈkæp.(t)ʃɚ/, essentially /ɪˈsɛnʃəli/, variation /ˌvɛəɹiˈeɪʃn̩/, automation /ˌɔ.təˈmeɪ.ʃən/, transactional /trænˈzækʃ(ə)nəl/
- Uzun kelimeler — vurguyu doğru yere koyun: literacy /ˈlɪtəɹəsi/, analytics /ˌæn.əˈlɪt.ɪks/, analytical /ˌæn.əˈlɪt.ɪ.kəl/, primarily /ˈpɹaɪ.mə.ɹə.li/, essentially /ɪˈsɛnʃəli/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- 0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; database, warehouse, literacy gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.